





'''simple docstring'''


import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Tuple )							->     int:

       """simple docstring"""


       return 1.0 / (1.0 + np.exp(-_outputs ))
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			List[Any] )							->     List[Any]:

       """simple docstring"""


       UpperCAmelCase  =						np.max(_outputs			,	axis=-1			,	keepdims=SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						np.exp(_outputs - maxes )
       return shifted_exp / shifted_exp.sum(axis=-1			,	keepdims=SCREAMING_SNAKE_CASE_ )






class     lowerCAmelCase__ (				UpperCAmelCase_    ):

       '''simple docstring'''
       _lowerCamelCase								='''sigmoid'''
       _lowerCamelCase								='''softmax'''
       _lowerCamelCase								='''none'''






@add_end_docstrings(
    UpperCAmelCase_      , r"\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    "      , )
class     lowerCAmelCase__ (				UpperCAmelCase_    ):

       '''simple docstring'''
       _lowerCamelCase								=False
       _lowerCamelCase								=ClassificationFunction.NONE

       def __init__( self					:				Dict       ,     **a__					:				Any						):
              super().__init__(**a__						)

              self.check_model_type(
                  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
                  if self.framework == '''tf'''
                  else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING						)

       def 			__snake_case      ( self					:				Optional[int]       ,     a__					:				List[Any]=None       ,     a__					:				Optional[int]=None       ,     a__					:				Tuple=""       ,     **a__					:				Optional[int]						):
              UpperCAmelCase  =						tokenizer_kwargs

              UpperCAmelCase  =						{}
              if hasattr(self.model.config       ,     '''return_all_scores'''						) and return_all_scores is None:
                     UpperCAmelCase  =						self.model.config.return_all_scores

              if isinstance(a__       ,     a__						) or top_k is None:
                     UpperCAmelCase  =						top_k
                     UpperCAmelCase  =						False
              elif return_all_scores is not None:
                     warnings.warn(
                         '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
                         ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''       ,     a__       ,     )
                     if return_all_scores:
                            UpperCAmelCase  =						None
                     else:
                            UpperCAmelCase  =						1

              if isinstance(a__       ,     a__						):
                     UpperCAmelCase  =						ClassificationFunction[function_to_apply.upper()]

              if function_to_apply is not None:
                     UpperCAmelCase  =						function_to_apply
              return preprocess_params, {}, postprocess_params

       def __call__( self					:				Any       ,     *a__					:				List[Any]       ,     **a__					:				Union[str, Any]						):
              UpperCAmelCase  =						super().__call__(*a__       ,     **a__						)
              # TODO try and retrieve it in a nicer way from _sanitize_parameters.
              UpperCAmelCase  =						'''top_k''' not in kwargs
              if isinstance(args[0]       ,     a__						) and _legacy:
                     # This pipeline is odd, and return a list when single item is run
                     return [result]
              else:
                     return result

       def 			__snake_case      ( self					:				List[Any]       ,     a__					:				Optional[Any]       ,     **a__					:				Dict						):
              UpperCAmelCase  =						self.framework
              if isinstance(a__       ,     a__						):
                     return self.tokenizer(**a__       ,     return_tensors=a__       ,     **a__						)
              elif isinstance(a__       ,     a__						) and len(a__						) == 1 and isinstance(inputs[0]       ,     a__						) and len(inputs[0]						) == 2:
                     # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
                     return self.tokenizer(
                         text=inputs[0][0]       ,     text_pair=inputs[0][1]       ,     return_tensors=a__       ,     **a__						)
              elif isinstance(a__       ,     a__						):
                     # This is likely an invalid usage of the pipeline attempting to pass text pairs.
                     raise ValueError(
                         '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
                         ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'''						)
              return self.tokenizer(a__       ,     return_tensors=a__       ,     **a__						)

       def 			__snake_case      ( self					:				str       ,     a__					:				Union[str, Any]						):
              return self.model(**a__						)



       def 			__snake_case      ( self					:				int       ,     a__					:				int       ,     a__					:				int=None       ,     a__					:				Union[str, Any]=1       ,     a__					:				Optional[int]=True						):


              if function_to_apply is None:
                     if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                            UpperCAmelCase  =						ClassificationFunction.SIGMOID
                     elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                            UpperCAmelCase  =						ClassificationFunction.SOFTMAX
                     elif hasattr(self.model.config       ,     '''function_to_apply'''						) and function_to_apply is None:
                            UpperCAmelCase  =						self.model.config.function_to_apply
                     else:
                            UpperCAmelCase  =						ClassificationFunction.NONE

              UpperCAmelCase  =						model_outputs['''logits'''][0]
              UpperCAmelCase  =						outputs.numpy()

              if function_to_apply == ClassificationFunction.SIGMOID:
                     UpperCAmelCase  =						sigmoid(a__						)
              elif function_to_apply == ClassificationFunction.SOFTMAX:
                     UpperCAmelCase  =						softmax(a__						)
              elif function_to_apply == ClassificationFunction.NONE:
                     UpperCAmelCase  =						outputs
              else:
                     raise ValueError(f"Unrecognized `function_to_apply` argument: {function_to_apply}"						)

              if top_k == 1 and _legacy:
                     return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

              UpperCAmelCase  =						[
                  {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(a__						)
              ]
              if not _legacy:
                     dict_scores.sort(key=lambda a__						: x["score"]       ,     reverse=a__						)
                     if top_k is not None:
                            UpperCAmelCase  =						dict_scores[:top_k]
              return dict_scores








'''simple docstring'''







import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()

class 	_snake_case(			UpperCAmelCase       , UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							Union[str, Any]						=					StableUnCLIPPipeline
 __snake_case:							List[str]						=					TEXT_TO_IMAGE_PARAMS
 __snake_case:							str						=					TEXT_TO_IMAGE_BATCH_PARAMS
 __snake_case:							Dict						=					TEXT_TO_IMAGE_IMAGE_PARAMS
 __snake_case:							List[str]						=					TEXT_TO_IMAGE_IMAGE_PARAMS

 # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
 __snake_case:							str						=					False






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   int:






    """simple docstring"""


    A__       =     32
    A__       =     embedder_hidden_size

    # prior components

    torch.manual_seed(0    )
    A__       =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

    torch.manual_seed(0    )
    A__       =     CLIPTextModelWithProjection(
        CLIPTextConfig(
            bos_token_id=0    ,		eos_token_id=2    ,		hidden_size=a    ,		projection_dim=a    ,		intermediate_size=37    ,		layer_norm_eps=1e-05    ,		num_attention_heads=4    ,		num_hidden_layers=5    ,		pad_token_id=1    ,		vocab_size=10_00    ,		)    )

    torch.manual_seed(0    )
    A__       =     PriorTransformer(
        num_attention_heads=2    ,		attention_head_dim=12    ,		embedding_dim=a    ,		num_layers=1    ,		)

    torch.manual_seed(0    )
    A__       =     DDPMScheduler(
        variance_type='fixed_small_log'    ,		prediction_type='sample'    ,		num_train_timesteps=10_00    ,		clip_sample=a    ,		clip_sample_range=5.0    ,		beta_schedule='squaredcos_cap_v2'    ,		)

    # regular denoising components

    torch.manual_seed(0    )
    A__       =     StableUnCLIPImageNormalizer(embedding_dim=a    )
    A__       =     DDPMScheduler(beta_schedule='squaredcos_cap_v2'    )

    torch.manual_seed(0    )
    A__       =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

    torch.manual_seed(0    )
    A__       =     CLIPTextModel(
        CLIPTextConfig(
            bos_token_id=0    ,		eos_token_id=2    ,		hidden_size=a    ,		projection_dim=32    ,		intermediate_size=37    ,		layer_norm_eps=1e-05    ,		num_attention_heads=4    ,		num_hidden_layers=5    ,		pad_token_id=1    ,		vocab_size=10_00    ,		)    )

    torch.manual_seed(0    )
    A__       =     UNetaDConditionModel(
        sample_size=32    ,		in_channels=4    ,		out_channels=4    ,		down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')    ,		up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')    ,		block_out_channels=(32, 64)    ,		attention_head_dim=(2, 4)    ,		class_embed_type='projection'    ,		projection_class_embeddings_input_dim=embedder_projection_dim * 2    ,		cross_attention_dim=a    ,		layers_per_block=1    ,		upcast_attention=a    ,		use_linear_projection=a    ,		)

    torch.manual_seed(0    )
    A__       =     DDIMScheduler(
        beta_schedule='scaled_linear'    ,		beta_start=0.0_0085    ,		beta_end=0.012    ,		prediction_type='v_prediction'    ,		set_alpha_to_one=a    ,		steps_offset=1    ,		)

    torch.manual_seed(0    )
    A__       =     AutoencoderKL()

    A__       =     {
        # prior components
        'prior_tokenizer': prior_tokenizer,
        'prior_text_encoder': prior_text_encoder,
        'prior': prior,
        'prior_scheduler': prior_scheduler,
        # image noising components
        'image_normalizer': image_normalizer,
        'image_noising_scheduler': image_noising_scheduler,
        # regular denoising components
        'tokenizer': tokenizer,
        'text_encoder': text_encoder,
        'unet': unet,
        'scheduler': scheduler,
        'vae': vae,
    }

    return components






 def 						_UpperCamelCase (self       :  List[Any]    ,		a       :  Optional[Any]    ,		a       :  str=0    )       ->   Dict:






    """simple docstring"""


    if str(a    ).startswith('mps'    ):
       A__       =     torch.manual_seed(a    )
    else:
       A__       =     torch.Generator(device=a    ).manual_seed(a    )
    A__       =     {
        'prompt': 'A painting of a squirrel eating a burger',
        'generator': generator,
        'num_inference_steps': 2,
        'prior_num_inference_steps': 2,
        'output_type': 'numpy',
    }
    return inputs






 def 						_UpperCamelCase (self       :  List[str]    )       ->   Optional[int]:






    """simple docstring"""


    A__       =     torch_device == 'cpu'

    self._test_attention_slicing_forward_pass(test_max_difference=a    )






 def 						_UpperCamelCase (self       :  Dict    )       ->   str:






    """simple docstring"""


    A__       =     torch_device in ['cpu', 'mps']

    self._test_inference_batch_single_identical(test_max_difference=a    )




@slow
@require_torch_gpu
class 	_snake_case(			unittest.TestCase      ):






 def 						_UpperCamelCase (self       :  Any    )       ->   Optional[Any]:






    """simple docstring"""


    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()






 def 						_UpperCamelCase (self       :  Dict    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'    )

    A__       =     StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'    ,		torch_dtype=torch.floataa    )
    pipe.to(a    )
    pipe.set_progress_bar_config(disable=a    )
    # stable unclip will oom when integration tests are run on a V100,
    # so turn on memory savings
    pipe.enable_attention_slicing()
    pipe.enable_sequential_cpu_offload()

    A__       =     torch.Generator(device='cpu'    ).manual_seed(0    )
    A__       =     pipe('anime turle'    ,		generator=a    ,		output_type='np'    )

    A__       =     output.images[0]

    assert image.shape == (7_68, 7_68, 3)

    assert_mean_pixel_difference(a    ,		a    )






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   str:






    """simple docstring"""


    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    A__       =     StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'    ,		torch_dtype=torch.floataa    )
    A__       =     pipe.to(a    )
    pipe.set_progress_bar_config(disable=a    )
    pipe.enable_attention_slicing()
    pipe.enable_sequential_cpu_offload()

    A__       =     pipe(
        'anime turtle'    ,		prior_num_inference_steps=2    ,		num_inference_steps=2    ,		output_type='np'    ,		)

    A__       =     torch.cuda.max_memory_allocated()
    # make sure that less than 7 GB is allocated
    assert mem_bytes < 7 * 10**9


